The indexing and retrieval of images is either context-based dependent on literal key words, or is content-based dependent on quantifiable visual features of the image.
Context-base searches use textual information about images to search. This requires human users to type in terms that personally describe every image in the database. One problem with the personal description of every image is this becomes impractical for very large databases and also impractical for images that are generate a large number of images automatically, such as images from surveillance cameras. Another problem with context-based systems is that different descriptions may use different synonyms to describe similar images, causing separate searches based on different descriptions to miss similar images.
A context-based search may also include query by example which uses a sample image as the basis of a search. One problem with such an underlying search algorithm is that the algorithm may depend on the application, such that images retrieved by a search all share common elements with the provided example. Examples of such images include preexisting images supplied by the user or chosen at random, or a drawn rough approximation of an image to be searched. Such a query technique may remove the difficulties that can arise when trying to describe images with words.
Content-based image retrieval, also referred to by the terms query by image content, or as content-based visual information retrieval, is a substitute for traditional text based search queries which use metadata captions or keywords of searching for digital images in large databases. Content-based searches analyze the actual contents of the image. The term ‘content’ refers to colors, shapes, textures, or information that may be derived from the image itself.
Content-based has been used to describe the process of retrieving desired images from a large collection on the basis of syntactical image features. The techniques, tools and algorithms that are used originate from fields such as statistics, pattern recognition, signal processing, and computer vision. Content-based image retrieval is an alternative to text-based metadata systems.
Most content-based methods are not suited for general class of images because they are based on verbal descriptions of the images or they access images based on color and texture. In the art, U.S. Pat. No. 7,079,686 to Ahmed classifies pixels in a document image as either an image or text and also incorporates image enhancement and background suppression, but does not incorporate general class imaging. Furthermore, U.S. Pat. No. 6,584,221 to Moghaddam uses an estimated joint distribution to classified images by regions of interest and extracts color and textual features, but does not incorporate general classes of images. Recent statistical advances in Nonlinear Principal Component Analysis (NLPCA) indicate that they may give a framework for improving the accuracy of image retrieval and may be suited for improving the content-based image retrieval.
The present application provides an improved system and method which overcomes the above-referenced methods for using the content of images to index and retrieve images in a computer operable database.
In accordance with one aspect, a method is presented for indexing, organizing, and retrieving the images stored in a database.
In accordance with one aspect, a system is presented for indexing, organizing, and retrieving the images stored in a database.
An advantage resides in providing a search means for locating an image based on non-keyword based parameters such as, but not limited to, color, shape, and texture. Such a search does not require a searcher to verbally describe the image being searched for during a search.
A further advantage resides in providing an optimum transform of the data involved in performing a search.
A still further advantage resides in providing relevance feedback to a search, which enables a searcher to narrow a search faster than when relevance is not provided during the search process.
A still further advantage is the search overcomes the limitations inherent in context-based search systems by applying NLPCA to facilitate the indexing and retrieval of image content.
The present application may take form in various components and arrangements of components, and in various steps and arrangement of steps. The drawings are only for purposes of illustrating the preferred embodiments and are not to be constructed as limiting the invention.